---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
base_model: bert-base-uncased
model-index:
- name: finetuning-sentiment-model-5000-samples
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# finetuning-sentiment-model-5000-samples

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0701
- Accuracy: 0.758
- F1: 0.7580

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 313  | 1.0216          | 0.744    | 0.744  |
| 0.2263        | 2.0   | 626  | 1.0701          | 0.758    | 0.7580 |
| 0.2263        | 3.0   | 939  | 1.3097          | 0.723    | 0.723  |
| 0.1273        | 4.0   | 1252 | 1.4377          | 0.743    | 0.743  |
| 0.051         | 5.0   | 1565 | 1.4884          | 0.739    | 0.739  |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1